def test_slice_theta_mm():
N = 100
data = np.array([(np.random.random() < 0.8, ) for _ in xrange(N)],
dtype=[('', bool)])
defn = model_definition(N, [bbnc])
r = rng()
prior = {'alpha': 1.0, 'beta': 9.0}
view = numpy_dataview(data)
s = initialize(defn,
view,
cluster_hp={
'alpha': 1.,
'beta': 9.
},
feature_hps=[prior],
r=r,
assignment=[0] * N)
heads = len([1 for y in data if y[0]])
tails = N - heads
alpha1 = prior['alpha'] + heads
beta1 = prior['beta'] + tails
bs = bind(s, view)
params = {0: {'p': 0.05}}
def sample_fn():
theta(bs, r, tparams=params)
return s.get_suffstats(0, 0)['p']
rv = beta(alpha1, beta1)
assert_1d_cont_dist_approx_sps(sample_fn, rv, nsamples=50000)
def _test_convergence_bb_cxx(N,
D,
kernel,
preprocess_data_fn=None,
nonconj=False,
burnin_niters=10000,
skip=10,
ntries=50,
nsamples=1000,
kl_places=2):
r = rng()
cluster_hp = {'alpha': 2.0}
feature_hps = [{'alpha': 1.0, 'beta': 1.0}] * D
defn = model_definition(N, [bb] * D)
nonconj_defn = model_definition(N, [bbnc] * D)
Y, posterior = data_with_posterior(defn, cluster_hp, feature_hps,
preprocess_data_fn)
data = numpy_dataview(Y)
s = initialize(nonconj_defn if nonconj else defn,
data,
cluster_hp=cluster_hp,
feature_hps=feature_hps,
r=r)
bs = bind(s, data)
wrapped_kernel = lambda s: kernel(s, r)
_test_convergence(bs, posterior, wrapped_kernel, burnin_niters, skip,
ntries, nsamples, kl_places)
def test_get_set_params():
defn = model_definition(1, [bb, bnb, gp, nich])
data = np.array([
(True, 3, 5, 10.),
],
dtype=[('', bool), ('', int), ('', int), ('', float)])
s = initialize(defn=defn, data=numpy_dataview(data), r=rng())
s.set_cluster_hp({'alpha': 3.0})
assert_dict_almost_equals(s.get_cluster_hp(), {'alpha': 3.0})
hyperparams = [
{
'alpha': 1.2,
'beta': 4.3
},
{
'alpha': 1.,
'beta': 1.,
'r': 1
},
{
'alpha': 1.,
'inv_beta': 1.
},
{
'mu': 30.,
'kappa': 1.,
'sigmasq': 1.,
'nu': 1.
},
]
for i, hp in enumerate(hyperparams):
s.set_feature_hp(i, hp)
assert_dict_almost_equals(s.get_feature_hp(i), hp)
def test_runner_multiprocessing_convergence():
N, D = 4, 5
defn = model_definition(N, [bb] * D)
prng = rng()
Y, posterior = data_with_posterior(defn, r=prng)
view = numpy_dataview(Y)
latents = [model.initialize(defn, view, prng)
for _ in xrange(mp.cpu_count())]
runners = [runner.runner(defn, view, latent, ['assign'])
for latent in latents]
r = parallel.runner(runners)
r.run(r=prng, niters=1000) # burnin
idmap = {C: i for i, C in enumerate(permutation_iter(N))}
def sample_iter():
r.run(r=prng, niters=10)
for latent in r.get_latents():
yield idmap[tuple(permutation_canonical(latent.assignments()))]
ref = [None]
def sample_fn():
if ref[0] is None:
ref[0] = sample_iter()
try:
return next(ref[0])
except StopIteration:
ref[0] = None
return sample_fn()
assert_discrete_dist_approx(sample_fn, posterior, ntries=100, kl_places=2)
def _test_convergence_bb_cxx(N,
D,
kernel,
preprocess_data_fn=None,
nonconj=False,
burnin_niters=10000,
skip=10,
ntries=50,
nsamples=1000,
kl_places=2):
r = rng()
cluster_hp = {'alpha': 2.0}
feature_hps = [{'alpha': 1.0, 'beta': 1.0}] * D
defn = model_definition(N, [bb] * D)
nonconj_defn = model_definition(N, [bbnc] * D)
Y, posterior = data_with_posterior(
defn, cluster_hp, feature_hps, preprocess_data_fn)
data = numpy_dataview(Y)
s = initialize(nonconj_defn if nonconj else defn,
data,
cluster_hp=cluster_hp,
feature_hps=feature_hps,
r=r)
bs = bind(s, data)
wrapped_kernel = lambda s: kernel(s, r)
_test_convergence(bs,
posterior,
wrapped_kernel,
burnin_niters,
skip,
ntries,
nsamples,
kl_places)
def test_slice_theta_mm():
N = 100
data = np.array(
[(np.random.random() < 0.8,) for _ in xrange(N)],
dtype=[('', bool)])
defn = model_definition(N, [bbnc])
r = rng()
prior = {'alpha': 1.0, 'beta': 9.0}
view = numpy_dataview(data)
s = initialize(
defn,
view,
cluster_hp={'alpha': 1., 'beta': 9.},
feature_hps=[prior],
r=r,
assignment=[0] * N)
heads = len([1 for y in data if y[0]])
tails = N - heads
alpha1 = prior['alpha'] + heads
beta1 = prior['beta'] + tails
bs = bind(s, view)
params = {0: {'p': 0.05}}
def sample_fn():
theta(bs, r, tparams=params)
return s.get_suffstats(0, 0)['p']
rv = beta(alpha1, beta1)
assert_1d_cont_dist_approx_sps(sample_fn, rv, nsamples=50000)
def crp_score(assignment):
latent = initialize(defn,
view,
r=r,
cluster_hp={'alpha': alpha},
assignment=assignment)
return latent.score_assignment()
def test_posterior_predictive_statistic():
N, D = 10, 4 # D needs to be even
defn = model_definition(N, [bb] * D)
Y = toy_dataset(defn)
prng = rng()
view = numpy_dataview(Y)
latents = [model.initialize(defn, view, prng) for _ in xrange(10)]
q = ma.masked_array(
np.array([(False,) * D], dtype=[('', bool)] * D),
mask=[(False,) * (D / 2) + (True,) * (D / 2)])
statistic = query.posterior_predictive_statistic(q, latents, prng)
assert_equals(statistic.shape, (1,))
assert_equals(len(statistic.dtype), D)
statistic = query.posterior_predictive_statistic(
q, latents, prng, merge='mode')
assert_equals(statistic.shape, (1,))
assert_equals(len(statistic.dtype), D)
statistic = query.posterior_predictive_statistic(
q, latents, prng, merge=['mode', 'mode', 'avg', 'avg'])
assert_equals(statistic.shape, (1,))
assert_equals(len(statistic.dtype), D)
q = ma.masked_array(
np.array([(False,) * D] * 3, dtype=[('', bool)] * D),
mask=[(False,) * (D / 2) + (True,) * (D / 2)] * 3)
statistic = query.posterior_predictive_statistic(q, latents, prng)
assert_equals(statistic.shape, (3,))
assert_equals(len(statistic.dtype), D)
def score_fn(assignment):
s = initialize(defn,
data,
r,
cluster_hp=cluster_hp,
feature_hps=feature_hps,
assignment=assignment)
return s.score_joint(r)
def test_zmatrix():
N, D = 10, 4
defn = model_definition(N, [bb] * D)
Y = toy_dataset(defn)
prng = rng()
view = numpy_dataview(Y)
latents = [model.initialize(defn, view, prng) for _ in xrange(10)]
zmat = query.zmatrix(latents)
assert_equals(zmat.shape, (N, N))
def test_runner_multyvac():
defn = model_definition(10, [bb, nich, niw(3)])
Y = toy_dataset(defn)
view = numpy_dataview(Y)
kc = runner.default_kernel_config(defn)
prng = rng()
latents = [model.initialize(defn, view, prng)
for _ in xrange(2)]
runners = [runner.runner(defn, view, latent, kc) for latent in latents]
r = parallel.runner(runners, backend='multyvac', layer='perf', core='f2')
r.run(r=prng, niters=1000)
r.run(r=prng, niters=1000)
def _test_scalar_hp_inference(view,
prior_fn,
w,
grid_min,
grid_max,
grid_n,
likelihood_model,
scalar_hp_key,
burnin=1000,
nsamples=1000,
every=10,
trials=100,
places=2):
"""
view must be 1D
"""
r = rng()
hparams = {0: {scalar_hp_key: (prior_fn, w)}}
def score_fn(scalar):
d = latent.get_feature_hp(0)
prev_scalar = d[scalar_hp_key]
d[scalar_hp_key] = scalar
latent.set_feature_hp(0, d)
score = prior_fn(scalar) + latent.score_data(0, None, r)
d[scalar_hp_key] = prev_scalar
latent.set_feature_hp(0, d)
return score
defn = model_definition(len(view), [likelihood_model])
latent = initialize(defn, view, r=r)
model = bind(latent, view)
def sample_fn():
for _ in xrange(every):
slice_hp(model, r, hparams=hparams)
return latent.get_feature_hp(0)[scalar_hp_key]
for _ in xrange(burnin):
slice_hp(model, r, hparams=hparams)
print 'finished burnin of', burnin, 'iterations'
print 'grid_min', grid_min, 'grid_max', grid_max
assert_1d_cont_dist_approx_emp(sample_fn,
score_fn,
grid_min,
grid_max,
grid_n,
trials,
nsamples,
places)
def test_runner_multiprocessing():
defn = model_definition(10, [bb, nich, niw(3)])
Y = toy_dataset(defn)
view = numpy_dataview(Y)
kc = runner.default_kernel_config(defn)
prng = rng()
latents = [model.initialize(defn, view, prng)
for _ in xrange(mp.cpu_count())]
runners = [runner.runner(defn, view, latent, kc) for latent in latents]
r = parallel.runner(runners)
# check it is restartable
r.run(r=prng, niters=10)
r.run(r=prng, niters=10)
def run_dpgmm(niter=1000, datadir="../../", nfeatures=13):
ranking = [10, 6, 7, 26, 5, 8, 4, 19, 12, 23, 24, 33, 28, 25,
14, 3, 0, 1, 21, 30, 11, 31, 13, 9, 22, 2, 27, 29,
32, 17, 18, 20, 16, 15]
features, labels, lc, hr, tstart, \
features_lb, labels_lb, lc_lb, hr_lb, \
fscaled, fscaled_lb, fscaled_full, labels_all = \
load_data(datadir, tseg=1024.0, log_features=None,
ranking=ranking)
labels_phys = feature_engineering.convert_labels_to_physical(labels)
labels_phys_lb = feature_engineering.convert_labels_to_physical(labels_lb)
labels_all_phys = np.hstack([labels_phys["train"], labels_phys["val"],
labels_phys["test"]])
fscaled_small = fscaled_full[:, :13]
nchains = 8
# The random state object
prng = rng()
# Define a DP-GMM where the Gaussian is 2D
defn = model_definition(fscaled_small.shape[0],
[normal_inverse_wishart(fscaled_small.shape[1])])
fscaled_rec = np.array([(list(f),) for f in fscaled_small],
dtype=[('', np.float32, fscaled_small.shape[1])])
# Create a wrapper around the numpy recarray which
# data-microscopes understands
view = numpy_dataview(fscaled_rec)
# Initialize nchains start points randomly in the state space
latents = [model.initialize(defn, view, prng) for _ in xrange(nchains)]
# Create a runner for each chain
runners = [runner.runner(defn, view, latent,
kernel_config=['assign']) for latent in latents]
r = parallel.runner(runners)
r.run(r=prng, niters=niter)
with open(datadir+"grs1915_dpgmm.pkl", "w") as f:
pickle.dump(r, f)
return
def test_get_set_params():
defn = model_definition(1, [bb, bnb, gp, nich])
data = np.array([(True, 3, 5, 10.), ],
dtype=[('', bool), ('', int), ('', int), ('', float)])
s = initialize(defn=defn, data=numpy_dataview(data), r=rng())
s.set_cluster_hp({'alpha': 3.0})
assert_dict_almost_equals(s.get_cluster_hp(), {'alpha': 3.0})
hyperparams = [
{'alpha': 1.2, 'beta': 4.3},
{'alpha': 1., 'beta': 1., 'r': 1},
{'alpha': 1., 'inv_beta': 1.},
{'mu': 30., 'kappa': 1., 'sigmasq': 1., 'nu': 1.},
]
for i, hp in enumerate(hyperparams):
s.set_feature_hp(i, hp)
assert_dict_almost_equals(s.get_feature_hp(i), hp)
def _test_scalar_hp_inference(view,
prior_fn,
w,
grid_min,
grid_max,
grid_n,
likelihood_model,
scalar_hp_key,
burnin=1000,
nsamples=1000,
every=10,
trials=100,
places=2):
"""
view must be 1D
"""
r = rng()
hparams = {0: {scalar_hp_key: (prior_fn, w)}}
def score_fn(scalar):
d = latent.get_feature_hp(0)
prev_scalar = d[scalar_hp_key]
d[scalar_hp_key] = scalar
latent.set_feature_hp(0, d)
score = prior_fn(scalar) + latent.score_data(0, None, r)
d[scalar_hp_key] = prev_scalar
latent.set_feature_hp(0, d)
return score
defn = model_definition(len(view), [likelihood_model])
latent = initialize(defn, view, r=r)
model = bind(latent, view)
def sample_fn():
for _ in xrange(every):
slice_hp(model, r, hparams=hparams)
return latent.get_feature_hp(0)[scalar_hp_key]
for _ in xrange(burnin):
slice_hp(model, r, hparams=hparams)
print 'finished burnin of', burnin, 'iterations'
print 'grid_min', grid_min, 'grid_max', grid_max
assert_1d_cont_dist_approx_emp(sample_fn, score_fn, grid_min, grid_max,
grid_n, trials, nsamples, places)
def test_runner_convergence():
N, D = 4, 5
defn = model_definition(N, [bb] * D)
prng = rng()
Y, posterior = data_with_posterior(defn, r=prng)
view = numpy_dataview(Y)
latent = model.initialize(defn, view, prng)
r = runner.runner(defn, view, latent, ['assign'])
r.run(r=prng, niters=1000) # burnin
idmap = {C: i for i, C in enumerate(permutation_iter(N))}
def sample_fn():
r.run(r=prng, niters=10)
new_latent = r.get_latent()
return idmap[tuple(permutation_canonical(new_latent.assignments()))]
assert_discrete_dist_approx(sample_fn, posterior, ntries=100)
def latent(groups, entities_per_group, features, r):
N = groups * entities_per_group
defn = model_definition(N, [bb] * features)
# generate fake data
Y = np.random.random(size=(N, features)) <= 0.5
view = numpy_dataview(
np.array([tuple(y) for y in Y], dtype=[('', bool)] * features))
# assign entities to their respective groups
assignment = [[g] * entities_per_group for g in xrange(groups)]
assignment = list(it.chain.from_iterable(assignment))
latent = bind(initialize(defn, view, r, assignment=assignment), view)
latent.create_group(r) # perftest() doesnt modify group assignments
return latent
def test_posterior_predictive():
N, D = 10, 4 # D needs to be even
defn = model_definition(N, [bb] * D)
Y = toy_dataset(defn)
prng = rng()
view = numpy_dataview(Y)
latents = [model.initialize(defn, view, prng) for _ in xrange(10)]
q = ma.masked_array(
np.array([(False,) * D], dtype=[('', bool)] * D),
mask=[(False,) * (D / 2) + (True,) * (D / 2)])
samples = query.posterior_predictive(q, latents, prng)
assert_equals(samples.shape, (1, len(latents)))
q = ma.masked_array(
np.array([(False,) * D] * 3, dtype=[('', bool)] * D),
mask=[(False,) * (D / 2) + (True,) * (D / 2)] * 3)
samples = query.posterior_predictive(q, latents, prng)
assert_equals(samples.shape, (3, len(latents)))
def _test_runner_kernel_config(kc_fn, models):
defn = model_definition(10, models)
Y = toy_dataset(defn)
view = numpy_dataview(Y)
kc = kc_fn(defn)
prng = rng()
ntries = 5
while ntries:
latent = model.initialize(defn, view, prng)
assignments = latent.assignments()
r = runner.runner(defn, view, latent, kc)
r.run(r=prng, niters=10)
assignments1 = r.get_latent().assignments()
# XXX: it should be very unlikely the assignments are all equal
if assignments == assignments1:
ntries -= 1
else:
return # success
assert_true(False) # exceeded ntries
def test_mnist_supervised(n):
mnist_dataset = _get_mnist_dataset()
classes = range(10)
classmap = {c: i for i, c in enumerate(classes)}
train_data, test_data = [], []
for c in classes:
Y = mnist_dataset['data'][np.where(
mnist_dataset['target'] == float(c))[0]]
Y_train, Y_test = train_test_split(Y, test_size=0.01)
train_data.append(Y_train)
test_data.append(Y_test)
sample_size_max = n
def mk_class_data(c, Y):
n, D = Y.shape
print 'number of digit', c, 'in training is', n
dtype = [('', bool)] * D + [('', int)]
inds = np.random.permutation(Y.shape[0])[:sample_size_max]
Y = np.array([tuple(list(y) + [classmap[c]]) for y in Y[inds]],
dtype=dtype)
return Y
Y_train = np.hstack(
[mk_class_data(c, y) for c, y in zip(classes, train_data)])
Y_train = Y_train[np.random.permutation(np.arange(Y_train.shape[0]))]
n, = Y_train.shape
D = len(Y_train.dtype)
print 'training data is', n, 'examples'
print 'image dimension is', (D - 1), 'pixels'
view = numpy_dataview(Y_train)
defn = model_definition(n, [bb] * (D - 1) + [dd(len(classes))])
r = rng()
s = initialize(defn,
view,
cluster_hp={'alpha': 0.2},
feature_hps=[{
'alpha': 1.,
'beta': 1.
}] * (D - 1) + [{
'alphas': [1. for _ in classes]
}],
r=r)
bound_s = bind(s, view)
indiv_prior_fn = log_exponential(1.2)
hparams = {
i: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
}
for i in xrange(D - 1)
}
hparams[D - 1] = {
'alphas[{}]'.format(idx): (indiv_prior_fn, 1.5)
for idx in xrange(len(classes))
}
def print_prediction_results():
results = []
for c, Y_test in zip(classes, test_data):
for y in Y_test:
query = ma.masked_array(
np.array([tuple(y) + (0, )],
dtype=[('', bool)] * (D - 1) + [('', int)]),
mask=[(False, ) * (D - 1) + (True, )])[0]
samples = [
s.sample_post_pred(query, r)[1][0][-1] for _ in xrange(30)
]
samples = np.bincount(samples, minlength=len(classes))
prediction = np.argmax(samples)
results.append((classmap[c], prediction, samples))
print 'finished predictions for class', c
Y_actual = np.array([a for a, _, _ in results], dtype=np.int)
Y_pred = np.array([b for _, b, _ in results], dtype=np.int)
print 'accuracy:', accuracy_score(Y_actual, Y_pred)
print 'confusion matrix:'
print confusion_matrix(Y_actual, Y_pred)
# AUROC for one vs all (each class)
for i, clabel in enumerate(classes):
Y_true = np.copy(Y_actual)
# treat class c as the "positive" example
positive_examples = Y_actual == i
negative_examples = Y_actual != i
Y_true[positive_examples] = 1
Y_true[negative_examples] = 0
Y_prob = np.array([float(c[i]) / c.sum() for _, _, c in results])
cls_auc = roc_auc_score(Y_true, Y_prob)
print 'class', clabel, 'auc=', cls_auc
#import matplotlib.pylab as plt
#Y_prob = np.array([c for _, _, c in results])
#fpr, tpr, thresholds = roc_curve(Y_actual, Y_prob, pos_label=0)
#plt.plot(fpr, tpr)
#plt.show()
def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'
# training
iters = 30
for rid in xrange(iters):
kernel(rid)
# print group size breakdown
sizes = [(gid, s.groupsize(gid)) for gid in s.groups()]
sizes = sorted(sizes, key=lambda x: x[1], reverse=True)
print ' group_sizes=', sizes
#print_prediction_results()
# save state
mkdirp("mnist-states")
fname = os.path.join("mnist-states", "state-iter{}.ser".format(rid))
with open(fname, "w") as fp:
fp.write(s.serialize())
def crp_score(assignment):
latent = initialize(
defn, view, r=r,
cluster_hp={'alpha': alpha}, assignment=assignment)
return latent.score_assignment()
def test_mnist():
import matplotlib.pylab as plt
from PIL import Image, ImageOps
mnist_dataset = _get_mnist_dataset()
Y_2 = mnist_dataset['data'][np.where(mnist_dataset['target'] == 2.)[0]]
Y_3 = mnist_dataset['data'][np.where(mnist_dataset['target'] == 3.)[0]]
print 'number of twos:', Y_2.shape[0]
print 'number of threes:', Y_3.shape[0]
_, D = Y_2.shape
W = int(math.sqrt(D))
assert W * W == D
dtype = [('', bool)] * D
Y = np.vstack([Y_2, Y_3])
Y = np.array(
[tuple(y) for y in Y[np.random.permutation(np.arange(Y.shape[0]))]],
dtype=dtype)
view = numpy_dataview(Y)
defn = model_definition(Y.shape[0], [bb] * D)
r = rng()
s = initialize(
defn,
view,
cluster_hp={'alpha': 0.2},
feature_hps=[{'alpha': 1., 'beta': 1.}] * D,
r=r)
bound_s = bind(s, view)
indiv_prior_fn = log_exponential(1.2)
hparams = {
i: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
} for i in xrange(D)}
def plot_clusters(s, fname, scalebysize=False):
hps = [s.get_feature_hp(i) for i in xrange(D)]
def prior_prob(hp):
return hp['alpha'] / (hp['alpha'] + hp['beta'])
def data_for_group(gid):
suffstats = [s.get_suffstats(gid, i) for i in xrange(D)]
def prob(hp, ss):
top = hp['alpha'] + ss['heads']
bot = top + hp['beta'] + ss['tails']
return top / bot
probs = [prob(hp, ss) for hp, ss in zip(hps, suffstats)]
return np.array(probs)
def scale(d, weight):
im = d.reshape((W, W))
newW = max(int(weight * W), 1)
im = Image.fromarray(im)
im = im.resize((newW, newW))
im = ImageOps.expand(im, border=(W - newW) / 2)
im = np.array(im)
a, b = im.shape
#print 'a,b:', a, b
if a < W:
im = np.append(im, np.zeros(b)[np.newaxis, :], axis=0)
elif a > W:
im = im[:W, :]
assert im.shape[0] == W
if b < W:
#print 'current:', im.shape
im = np.append(im, np.zeros(W)[:, np.newaxis], axis=1)
elif b > W:
im = im[:, :W]
assert im.shape[1] == W
return im.flatten()
data = [(data_for_group(g), cnt) for g, cnt in groupsbysize(s)]
largest = max(cnt for _, cnt in data)
data = [scale(d, cnt / float(largest)) if scalebysize else d
for d, cnt in data]
digits_per_row = 12
rem = len(data) % digits_per_row
if rem:
fill = digits_per_row - rem
for _ in xrange(fill):
data.append(np.zeros(D))
assert not (len(data) % digits_per_row)
#rows = len(data) / digits_per_row
data = np.vstack([np.hstack([d.reshape((W, W))
for d in data[i:i + digits_per_row]])
for i in xrange(0, len(data), digits_per_row)])
#print 'saving figure', fname
plt.imshow(data, cmap=plt.cm.binary, interpolation='nearest')
plt.savefig(fname)
plt.close()
def plot_hyperparams(s, fname):
hps = [s.get_feature_hp(i) for i in xrange(D)]
alphas = np.array([hp['alpha'] for hp in hps])
betas = np.array([hp['beta'] for hp in hps])
data = np.hstack([alphas.reshape((W, W)), betas.reshape((W, W))])
plt.imshow(data, interpolation='nearest')
plt.colorbar()
plt.savefig(fname)
plt.close()
def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'
return s.score_joint(r)
# burnin
burnin = 20
for rid in xrange(burnin):
print 'score:', kernel(rid)
print 'finished burnin'
plot_clusters(s, 'mnist_clusters.pdf')
plot_clusters(s, 'mnist_clusters_bysize.pdf', scalebysize=True)
plot_hyperparams(s, 'mnist_hyperparams.pdf')
print 'groupcounts:', groupcounts(s)
# posterior predictions
present = D / 2
absent = D - present
queries = [tuple(Y_2[i]) for i in np.random.permutation(Y_2.shape[0])[:4]] + \
[tuple(Y_3[i]) for i in np.random.permutation(Y_3.shape[0])[:4]]
queries_masked = ma.masked_array(
np.array(queries, dtype=[('', bool)] * D),
mask=[(False,) * present + (True,) * absent])
def postpred_sample(y_new):
Y_samples = [s.sample_post_pred(y_new, r)[1] for _ in xrange(1000)]
Y_samples = np.array([list(y) for y in np.hstack(Y_samples)])
Y_avg = Y_samples.mean(axis=0)
return Y_avg
queries_masked = [postpred_sample(y) for y in queries_masked]
data0 = np.hstack([q.reshape((W, W)) for q in queries_masked])
data1 = np.hstack(
[np.clip(np.array(q, dtype=np.float), 0., 1.).reshape((W, W))
for q in queries])
data = np.vstack([data0, data1])
plt.imshow(data, cmap=plt.cm.binary, interpolation='nearest')
plt.savefig('mnist_predict.pdf')
plt.close()
def test_mnist_supervised():
mnist_dataset = _get_mnist_dataset()
classes = range(10)
classmap = {c: i for i, c in enumerate(classes)}
train_data, test_data = [], []
for c in classes:
Y = mnist_dataset['data'][
np.where(mnist_dataset['target'] == float(c))[0]]
Y_train, Y_test = train_test_split(Y, test_size=0.01)
train_data.append(Y_train)
test_data.append(Y_test)
sample_size_max = 10000
def mk_class_data(c, Y):
n, D = Y.shape
print 'number of digit', c, 'in training is', n
dtype = [('', bool)] * D + [('', int)]
inds = np.random.permutation(Y.shape[0])[:sample_size_max]
Y = np.array([tuple(list(y) + [classmap[c]]) for y in Y[inds]],
dtype=dtype)
return Y
Y_train = np.hstack([mk_class_data(c, y)
for c, y in zip(classes, train_data)])
Y_train = Y_train[np.random.permutation(np.arange(Y_train.shape[0]))]
n, = Y_train.shape
D = len(Y_train.dtype)
print 'training data is', n, 'examples'
print 'image dimension is', (D - 1), 'pixels'
view = numpy_dataview(Y_train)
defn = model_definition(n, [bb] * (D - 1) + [dd(len(classes))])
r = rng()
s = initialize(defn,
view,
cluster_hp={'alpha': 0.2},
feature_hps=[{'alpha': 1., 'beta': 1.}] *
(D - 1) + [{'alphas': [1. for _ in classes]}],
r=r)
bound_s = bind(s, view)
indiv_prior_fn = log_exponential(1.2)
hparams = {
i: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
} for i in xrange(D - 1)}
hparams[D - 1] = {
'alphas[{}]'.format(idx): (indiv_prior_fn, 1.5)
for idx in xrange(len(classes))
}
def print_prediction_results():
results = []
for c, Y_test in zip(classes, test_data):
for y in Y_test:
query = ma.masked_array(
np.array([tuple(y) + (0,)],
dtype=[('', bool)] * (D - 1) + [('', int)]),
mask=[(False,) * (D - 1) + (True,)])[0]
samples = [
s.sample_post_pred(query, r)[1][0][-1] for _ in xrange(30)]
samples = np.bincount(samples, minlength=len(classes))
prediction = np.argmax(samples)
results.append((classmap[c], prediction, samples))
print 'finished predictions for class', c
Y_actual = np.array([a for a, _, _ in results], dtype=np.int)
Y_pred = np.array([b for _, b, _ in results], dtype=np.int)
print 'accuracy:', accuracy_score(Y_actual, Y_pred)
print 'confusion matrix:'
print confusion_matrix(Y_actual, Y_pred)
# AUROC for one vs all (each class)
for i, clabel in enumerate(classes):
Y_true = np.copy(Y_actual)
# treat class c as the "positive" example
positive_examples = Y_actual == i
negative_examples = Y_actual != i
Y_true[positive_examples] = 1
Y_true[negative_examples] = 0
Y_prob = np.array([float(c[i]) / c.sum() for _, _, c in results])
cls_auc = roc_auc_score(Y_true, Y_prob)
print 'class', clabel, 'auc=', cls_auc
#import matplotlib.pylab as plt
#Y_prob = np.array([c for _, _, c in results])
#fpr, tpr, thresholds = roc_curve(Y_actual, Y_prob, pos_label=0)
#plt.plot(fpr, tpr)
#plt.show()
def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'
# print group size breakdown
sizes = [(gid, s.groupsize(gid)) for gid in s.groups()]
sizes = sorted(sizes, key=lambda x: x[1], reverse=True)
print ' group_sizes=', sizes
print_prediction_results()
# save state
mkdirp("mnist-states")
fname = os.path.join("mnist-states", "state-iter{}.ser".format(rid))
with open(fname, "w") as fp:
fp.write(s.serialize())
# training
iters = 30
for rid in xrange(iters):
kernel(rid)
def test_mnist():
import matplotlib.pylab as plt
from PIL import Image, ImageOps
mnist_dataset = _get_mnist_dataset()
Y_2 = mnist_dataset['data'][np.where(mnist_dataset['target'] == 2.)[0]]
Y_3 = mnist_dataset['data'][np.where(mnist_dataset['target'] == 3.)[0]]
print 'number of twos:', Y_2.shape[0]
print 'number of threes:', Y_3.shape[0]
_, D = Y_2.shape
W = int(math.sqrt(D))
assert W * W == D
dtype = [('', bool)] * D
Y = np.vstack([Y_2, Y_3])
Y = np.array(
[tuple(y) for y in Y[np.random.permutation(np.arange(Y.shape[0]))]],
dtype=dtype)
view = numpy_dataview(Y)
defn = model_definition(Y.shape[0], [bb] * D)
r = rng()
s = initialize(defn,
view,
cluster_hp={'alpha': 0.2},
feature_hps=[{
'alpha': 1.,
'beta': 1.
}] * D,
r=r)
bound_s = bind(s, view)
indiv_prior_fn = log_exponential(1.2)
hparams = {
i: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
}
for i in xrange(D)
}
def plot_clusters(s, fname, scalebysize=False):
hps = [s.get_feature_hp(i) for i in xrange(D)]
def prior_prob(hp):
return hp['alpha'] / (hp['alpha'] + hp['beta'])
def data_for_group(gid):
suffstats = [s.get_suffstats(gid, i) for i in xrange(D)]
def prob(hp, ss):
top = hp['alpha'] + ss['heads']
bot = top + hp['beta'] + ss['tails']
return top / bot
probs = [prob(hp, ss) for hp, ss in zip(hps, suffstats)]
return np.array(probs)
def scale(d, weight):
im = d.reshape((W, W))
newW = max(int(weight * W), 1)
im = Image.fromarray(im)
im = im.resize((newW, newW))
im = ImageOps.expand(im, border=(W - newW) / 2)
im = np.array(im)
a, b = im.shape
#print 'a,b:', a, b
if a < W:
im = np.append(im, np.zeros(b)[np.newaxis, :], axis=0)
elif a > W:
im = im[:W, :]
assert im.shape[0] == W
if b < W:
#print 'current:', im.shape
im = np.append(im, np.zeros(W)[:, np.newaxis], axis=1)
elif b > W:
im = im[:, :W]
assert im.shape[1] == W
return im.flatten()
data = [(data_for_group(g), cnt) for g, cnt in groupsbysize(s)]
largest = max(cnt for _, cnt in data)
data = [
scale(d, cnt / float(largest)) if scalebysize else d
for d, cnt in data
]
digits_per_row = 12
rem = len(data) % digits_per_row
if rem:
fill = digits_per_row - rem
for _ in xrange(fill):
data.append(np.zeros(D))
assert not (len(data) % digits_per_row)
#rows = len(data) / digits_per_row
data = np.vstack([
np.hstack([d.reshape((W, W)) for d in data[i:i + digits_per_row]])
for i in xrange(0, len(data), digits_per_row)
])
#print 'saving figure', fname
plt.imshow(data, cmap=plt.cm.binary, interpolation='nearest')
plt.savefig(fname)
plt.close()
def plot_hyperparams(s, fname):
hps = [s.get_feature_hp(i) for i in xrange(D)]
alphas = np.array([hp['alpha'] for hp in hps])
betas = np.array([hp['beta'] for hp in hps])
data = np.hstack([alphas.reshape((W, W)), betas.reshape((W, W))])
plt.imshow(data, interpolation='nearest')
plt.colorbar()
plt.savefig(fname)
plt.close()
def kernel(rid):
start0 = time.time()
assign(bound_s, r)
sec0 = time.time() - start0
start1 = time.time()
hp(bound_s, r, hparams=hparams)
sec1 = time.time() - start1
print 'rid=', rid, 'nclusters=', s.ngroups(), \
'iter0=', sec0, 'sec', 'iter1=', sec1, 'sec'
sec_per_post_pred = sec0 / (float(view.size()) * (float(s.ngroups())))
print ' time_per_post_pred=', sec_per_post_pred, 'sec'
return s.score_joint(r)
# burnin
burnin = 20
for rid in xrange(burnin):
print 'score:', kernel(rid)
print 'finished burnin'
plot_clusters(s, 'mnist_clusters.pdf')
plot_clusters(s, 'mnist_clusters_bysize.pdf', scalebysize=True)
plot_hyperparams(s, 'mnist_hyperparams.pdf')
print 'groupcounts:', groupcounts(s)
# posterior predictions
present = D / 2
absent = D - present
queries = [tuple(Y_2[i]) for i in np.random.permutation(Y_2.shape[0])[:4]] + \
[tuple(Y_3[i]) for i in np.random.permutation(Y_3.shape[0])[:4]]
queries_masked = ma.masked_array(np.array(queries, dtype=[('', bool)] * D),
mask=[(False, ) * present +
(True, ) * absent])
def postpred_sample(y_new):
Y_samples = [s.sample_post_pred(y_new, r)[1] for _ in xrange(1000)]
Y_samples = np.array([list(y) for y in np.hstack(Y_samples)])
Y_avg = Y_samples.mean(axis=0)
return Y_avg
queries_masked = [postpred_sample(y) for y in queries_masked]
data0 = np.hstack([q.reshape((W, W)) for q in queries_masked])
data1 = np.hstack([
np.clip(np.array(q, dtype=np.float), 0., 1.).reshape((W, W))
for q in queries
])
data = np.vstack([data0, data1])
plt.imshow(data, cmap=plt.cm.binary, interpolation='nearest')
plt.savefig('mnist_predict.pdf')
plt.close()
